hmdmahdavi/s1-thinking-distill-instruct-flash-cot
The hmdmahdavi/s1-thinking-distill-instruct-flash-cot is a 4 billion parameter instruction-tuned language model, fine-tuned by hmdmahdavi from the Qwen/Qwen3-4B-Instruct-2507 base model. With a 40960 token context length, this model is optimized for instruction following and general conversational tasks. It leverages SFT training with the TRL framework to enhance its response generation capabilities.
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Model Overview
The hmdmahdavi/s1-thinking-distill-instruct-flash-cot is a 4 billion parameter instruction-tuned language model, developed by hmdmahdavi. It is built upon the Qwen/Qwen3-4B-Instruct-2507 base model, inheriting its robust architecture and capabilities. The model has been specifically fine-tuned using the TRL (Transformer Reinforcement Learning) framework, indicating a focus on improving instruction adherence and response quality through supervised fine-tuning (SFT).
Key Capabilities
- Instruction Following: Designed to accurately interpret and respond to user instructions.
- General Text Generation: Capable of generating coherent and contextually relevant text for a variety of prompts.
- Extended Context: Supports a substantial context length of 40960 tokens, allowing for processing longer inputs and maintaining conversational history.
Training Details
The model underwent a supervised fine-tuning (SFT) process using the TRL library. This method typically involves training on a dataset of instruction-response pairs to align the model's output with human preferences and instructions. The training utilized specific versions of key frameworks including TRL 0.12.0, Transformers 4.57.3, Pytorch 2.5.1, Datasets 4.4.1, and Tokenizers 0.22.1.
Good For
- Applications requiring a compact yet capable instruction-following model.
- Tasks benefiting from a large context window for detailed interactions.
- General-purpose conversational AI and text generation where instruction adherence is crucial.